3 citations · 7 across the 14 of their papers we have counts for
4 papers · 1 filter
Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms
Shenao Zhang, Boyi Liu, Zhaoran Wang +1
ReParameterization (RP) Policy Gradient Methods (PGMs) have been widely adopted for continuous control tasks in robotics and computer graphics. However, recent studies have reveale…
Reason for Future, Act for Now: A Principled Framework for Autonomous LLM Agents with Provable Sample Efficiency
Zhihan Liu, Hao Hu, Shenao Zhang +4
Large language models (LLMs) demonstrate impressive reasoning abilities, but translating reasoning into actions in the real world remains challenging. In particular, it remains unc…
Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration
Zhihan Liu, Miao Lu, Wei Xiong +6
In online reinforcement learning (online RL), balancing exploration and exploitation is crucial for finding an optimal policy in a sample-efficient way. To achieve this, existing s…
Asking Before Acting: Gather Information in Embodied Decision Making with Language Models
Xiaoyu Chen, Shenao Zhang, Pushi Zhang +2
With strong capabilities of reasoning and a broad understanding of the world, Large Language Models (LLMs) have demonstrated immense potential in building versatile embodied decisi…